CN113139365A - Clinical trial subject recruitment method and device - Google Patents
Clinical trial subject recruitment method and device Download PDFInfo
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- CN113139365A CN113139365A CN202110369172.6A CN202110369172A CN113139365A CN 113139365 A CN113139365 A CN 113139365A CN 202110369172 A CN202110369172 A CN 202110369172A CN 113139365 A CN113139365 A CN 113139365A
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Abstract
The invention aims to provide a clinical trial subject recruitment method and equipment.
Description
Technical Field
The invention relates to a clinical trial subject recruitment method and device.
Background
At present, clinical test project recruitment subjects mainly send own medical records, examinations, inspection reports and the like to project recruitment personnel through the subjects, and the project recruitment personnel perform some simple labels on the subjects according to own experiences and then perform manual screening to match the labels in combination with the inclusion and exclusion standards of various projects on hands. The method has the following defects:
1. the method mainly adopts a manual screening mode, is time-consuming and labor-consuming, and is easy to make mistakes.
2. When the information of the subject library is more, the utilization rate of the history library is low. Resulting in some items that may have suitable subjects in the library that cannot be matched due to lack of convenient matching methods.
Disclosure of Invention
An object of the present invention is to provide a clinical trial subject recruitment method and apparatus.
According to one aspect of the present invention, there is provided a method of clinical trial subject recruitment, the method comprising:
converting medical data of a clinical trial subject into standard data;
setting a corresponding category label for standard data of a clinical trial subject;
matching the standard data of the clinical trial subjects with the corresponding clinical trial items based on the category labels.
Further, in the above method, setting a corresponding category label for the standard data of the clinical trial subjects includes:
acquiring first characteristic information in standard data of a clinical test subject;
and setting a corresponding category label for standard data of the clinical trial subjects based on the first characteristic information.
Further, in the above method, matching the standard data of the clinical test subjects with the corresponding clinical test items based on the category labels includes:
acquiring second characteristic information of the clinical test item;
and matching the category label with the second characteristic information to obtain a clinical test item corresponding to the standard data of the clinical test subject.
Further, in the above method, matching the standard data of the clinical test subjects with the corresponding clinical test items based on the category labels includes:
inputting the category label into a matching model,
judging whether the matching model matches a clinical test item corresponding to the standard data of the clinical test subject,
if yes, ending;
if not, after the matching model is subjected to correction training, the step of inputting the class label into the matching model is repeated.
According to another aspect of the present invention, there is also provided a clinical trial subject recruitment apparatus, wherein the apparatus comprises:
the conversion device is used for converting the medical data of the clinical trial subjects into standard data;
the label device is used for setting a corresponding category label for the standard data of the clinical trial subjects;
and the matching device is used for matching the standard data of the clinical test subjects with the corresponding clinical test items based on the class labels.
Further, in the above apparatus, the labeling device is configured to obtain first feature information in the standard data of the clinical trial subjects, and set corresponding category labels for the standard data of the clinical trial subjects based on the first feature information.
Further, in the above apparatus, the matching device is configured to obtain second characteristic information of the clinical trial item; and matching the category label with the second characteristic information to obtain a clinical test item corresponding to the standard data of the clinical test subject.
Further, in the above apparatus, the matching device is configured to input the category label into a matching model, determine whether the matching model matches a clinical test item corresponding to standard data of a clinical test subject, and if yes, end the process; if not, after the matching model is corrected and trained, the category labels are input into the matching model again, and whether the matching model matches the clinical test items corresponding to the standard data of the clinical test subjects is judged.
According to another aspect of the present invention, there is also provided a computing-based device, including:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
converting medical data of a clinical trial subject into standard data;
setting a corresponding category label for standard data of a clinical trial subject;
matching the standard data of the clinical trial subjects with the corresponding clinical trial items based on the category labels.
According to another aspect of the present invention, there is also provided a computer-readable storage medium having stored thereon computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, cause the processor to:
converting medical data of a clinical trial subject into standard data;
setting a corresponding category label for standard data of a clinical trial subject;
matching the standard data of the clinical trial subjects with the corresponding clinical trial items based on the category labels.
Compared with the prior art, the medical data of the clinical trial subjects are converted into the standard data, the professional category labels are made on the standard data of the trial subjects, and the category labels are automatically matched with the clinical trial items, so that the suitable trial subjects are quickly found for the clinical trial items, and meanwhile, the suitable clinical trial items are found for the trial subjects.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments made with reference to the following drawings:
fig. 1 shows a schematic diagram of a clinical trial subject recruitment method according to an embodiment of the invention.
The same or similar reference numbers in the drawings identify the same or similar elements.
Detailed Description
The present invention is described in further detail below with reference to the attached drawing figures.
In a typical configuration of the present application, the terminal, the device serving the network, and the trusted party each include one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory computer readable media (transient media), such as modulated data signals and carrier waves.
As shown in fig. 1, the present invention provides a clinical trial subject recruitment method comprising:
step S1, converting the medical data of the clinical trial subjects into standard data;
here, the medical data of the clinical trial subjects may be, for example: the medical information of the medical record, the examination and the inspection report can be converted into standard data by an OCR recognition pair mode;
specifically, a plurality of subjects can upload pathological/cytological reports, genetic testing reports, imaging reports, routine examination results, discharge plots and other pictures by filling in basic information (such as name, age, sex, city, indication and other information) of the subjects, and analyze the basic information into a standard data format (data comprises various indexes of examination, measurable lesion size and the like) through an OCR (optical character recognition) technology;
step S2, setting corresponding category labels for the standard data of the clinical trial subjects;
here, the category label may include: disease staging, gene expression, TNM staging, etc.;
and step S3, matching the standard data of the clinical test subjects with the corresponding clinical test items based on the category labels.
In the invention, medical data of the clinical trial subjects are converted into standard data, professional category labels are made on the standard data of the subjects, and the category labels are automatically matched with the clinical trial items, so that suitable subjects are quickly found for the clinical trial items, and simultaneously suitable clinical trial items are found for the subjects.
In an embodiment of the clinical trial subject recruitment method of the present invention, in step S2, the setting of the corresponding category label for the standard data of the clinical trial subjects includes:
step S21, acquiring first characteristic information in standard data of a clinical trial subject;
and step S22, setting corresponding class labels for the standard data of the clinical trial subjects based on the first characteristic information.
Here, the present embodiment can accurately set a corresponding category label to the standard data of the clinical trial participants by based on the first characteristic information in the standard data of the clinical trial participants.
In an embodiment of the clinical trial subject recruitment method of the present invention, step S3, based on the category labels, matches the standard data of the clinical trial subjects with corresponding clinical trial items, including:
step S311, acquiring second characteristic information of the clinical trial item;
here, the second characteristic information of the clinical trial item may be, for example: information such as target points and emission standards;
a standard medical language library may be established, and the second feature information in the standard medical language library may include: indications, age recruited, sex recruited, inclusion criteria, and the like;
and step S312, matching the category label with the second characteristic information to obtain a clinical test item corresponding to the standard data of the clinical test subject through matching.
Here, the labels may be based on the class of the subject such as: and matching the information of the indications, the city and the like, scoring, and sorting according to the score.
In this embodiment, by matching the category label with the second feature information, a clinical test item corresponding to the standard data of the clinical test subject can be accurately obtained through matching.
In an embodiment of the clinical trial subject recruitment method of the present invention, step S3, based on the category labels, matches the standard data of the clinical trial subjects with corresponding clinical trial items, including:
step S321, inputting the category label into a matching model,
step S322, judging whether the matching model matches the clinical test item corresponding to the standard data of the clinical test subject,
step S323, if yes, ending;
in step S324, if not, after performing correction training on the matching model, the process is repeated from step S321.
Here, in this embodiment, the matching model is modified and trained, so that the clinical test items corresponding to the standard data of the clinical test subjects can be more accurately matched.
As shown in fig. 1, the present invention provides a clinical trial subject recruitment device comprising:
the conversion device is used for converting the medical data of the clinical trial subjects into standard data;
here, the medical data of the clinical trial subjects may be, for example: the medical information of the medical record, the examination and the inspection report can be converted into standard data by an OCR recognition pair mode;
specifically, a plurality of subjects can upload pathological/cytological reports, genetic testing reports, imaging reports, routine examination results, discharge plots and other pictures by filling in basic information (such as name, age, sex, city, indication and other information) of the subjects, and analyze the basic information into a standard data format (data comprises various indexes of examination, measurable lesion size and the like) through an OCR (optical character recognition) technology;
the label device is used for setting a corresponding category label for the standard data of the clinical trial subjects;
here, the category label may include: disease staging, gene expression, TNM staging, etc.;
and the matching device is used for matching the standard data of the clinical test subjects with the corresponding clinical test items based on the class labels.
In the invention, medical data of the clinical trial subjects are converted into standard data, professional category labels are made on the standard data of the subjects, and the category labels are automatically matched with the clinical trial items, so that suitable subjects are quickly found for the clinical trial items, and simultaneously suitable clinical trial items are found for the subjects.
In an embodiment of the clinical trial subject recruitment device of the present invention, the tag device is configured to obtain first feature information in standard data of clinical trial subjects, and set a corresponding category tag for the standard data of the clinical trial subjects based on the first feature information.
Here, the present embodiment can accurately set a corresponding category label to the standard data of the clinical trial participants by based on the first characteristic information in the standard data of the clinical trial participants.
In an embodiment of the clinical trial subject recruitment device of the present invention, the matching device is configured to obtain second characteristic information of a clinical trial item; and matching the category label with the second characteristic information to obtain a clinical test item corresponding to the standard data of the clinical test subject.
Here, the second characteristic information of the clinical trial item may be, for example: information such as target points, emission standards and the like,
a standard medical language library may be established, and the second feature information in the standard medical language library may include: indications, age recruited, sex recruited, inclusion criteria, and the like;
the subject may be labeled according to their category such as: and matching the information of the indications, the city and the like, scoring, and sorting according to the score.
In this embodiment, by matching the category label with the second feature information, a clinical test item corresponding to the standard data of the clinical test subject can be accurately obtained through matching.
In an embodiment of the clinical trial subject recruitment device of the present invention, the matching device is configured to input the category label into a matching model, determine whether the matching model matches a clinical trial item corresponding to standard data of a clinical trial subject, and if yes, end the process; if not, after the matching model is corrected and trained, the category labels are input into the matching model again, and whether the matching model matches the clinical test items corresponding to the standard data of the clinical test subjects is judged.
Here, in this embodiment, the matching model is modified and trained, so that the clinical test items corresponding to the standard data of the clinical test subjects can be more accurately matched.
According to another aspect of the present invention, there is also provided a computing-based device, including:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
converting medical data of a clinical trial subject into standard data;
setting a corresponding category label for standard data of a clinical trial subject;
matching the standard data of the clinical trial subjects with the corresponding clinical trial items based on the category labels.
According to another aspect of the present invention, there is also provided a computer-readable storage medium having stored thereon computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, cause the processor to:
converting medical data of a clinical trial subject into standard data;
setting a corresponding category label for standard data of a clinical trial subject;
matching the standard data of the clinical trial subjects with the corresponding clinical trial items based on the category labels.
For details of embodiments of each device and storage medium of the present invention, reference may be made to corresponding parts of each method embodiment, and details are not described herein again.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
It should be noted that the present invention may be implemented in software and/or in a combination of software and hardware, for example, as an Application Specific Integrated Circuit (ASIC), a general purpose computer or any other similar hardware device. In one embodiment, the software program of the present invention may be executed by a processor to implement the steps or functions described above. Also, the software programs (including associated data structures) of the present invention can be stored in a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like. Further, some of the steps or functions of the present invention may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various steps or functions.
In addition, some of the present invention can be applied as a computer program product, such as computer program instructions, which when executed by a computer, can invoke or provide the method and/or technical solution according to the present invention through the operation of the computer. Program instructions which invoke the methods of the present invention may be stored on a fixed or removable recording medium and/or transmitted via a data stream on a broadcast or other signal-bearing medium and/or stored within a working memory of a computer device operating in accordance with the program instructions. An embodiment according to the invention herein comprises an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein the computer program instructions, when executed by the processor, trigger the apparatus to perform a method and/or solution according to embodiments of the invention as described above.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the apparatus claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Claims (10)
1. A method of clinical trial subject recruitment, wherein the method comprises:
converting medical data of a clinical trial subject into standard data;
setting a corresponding category label for standard data of a clinical trial subject;
matching the standard data of the clinical trial subjects with the corresponding clinical trial items based on the category labels.
2. The method of claim 1, wherein setting a corresponding category label to the standard data of the clinical trial subjects comprises:
acquiring first characteristic information in standard data of a clinical test subject;
and setting a corresponding category label for standard data of the clinical trial subjects based on the first characteristic information.
3. The method of claim 1, wherein matching the standard data of the clinical trial subjects to the corresponding clinical trial items based on the category label comprises:
acquiring second characteristic information of the clinical test item;
and matching the category label with the second characteristic information to obtain a clinical test item corresponding to the standard data of the clinical test subject.
4. The method of claim 1, wherein matching the standard data of the clinical trial subjects to the corresponding clinical trial items based on the category label comprises:
inputting the category label into a matching model,
judging whether the matching model matches a clinical test item corresponding to the standard data of the clinical test subject,
if yes, ending;
if not, after the matching model is subjected to correction training, the step of inputting the class label into the matching model is repeated.
5. A clinical trial subject recruitment device, wherein the device comprises:
the conversion device is used for converting the medical data of the clinical trial subjects into standard data;
the label device is used for setting a corresponding category label for the standard data of the clinical trial subjects;
and the matching device is used for matching the standard data of the clinical test subjects with the corresponding clinical test items based on the class labels.
6. The apparatus according to claim 5, wherein the label device is configured to obtain first feature information in the standard data of the clinical trial subjects, and set corresponding category labels for the standard data of the clinical trial subjects based on the first feature information.
7. The apparatus according to claim 5, wherein the matching device is configured to obtain second characteristic information of the clinical trial item; and matching the category label with the second characteristic information to obtain a clinical test item corresponding to the standard data of the clinical test subject.
8. The apparatus according to claim 5, wherein the matching device is configured to input the category label into a matching model, determine whether the matching model matches a clinical test item corresponding to standard data of a clinical test subject, and if so, end the process; if not, after the matching model is corrected and trained, the category labels are input into the matching model again, and whether the matching model matches the clinical test items corresponding to the standard data of the clinical test subjects is judged.
9. A computing-based device, comprising:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
converting medical data of a clinical trial subject into standard data;
setting a corresponding category label for standard data of a clinical trial subject;
matching the standard data of the clinical trial subjects with the corresponding clinical trial items based on the category labels.
10. A computer-readable storage medium having computer-executable instructions stored thereon, wherein the computer-executable instructions, when executed by a processor, cause the processor to:
converting medical data of a clinical trial subject into standard data;
setting a corresponding category label for standard data of a clinical trial subject;
matching the standard data of the clinical trial subjects with the corresponding clinical trial items based on the category labels.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114464327A (en) * | 2022-01-12 | 2022-05-10 | 上海柯林布瑞信息技术有限公司 | Method and device for screening subjects in clinical scientific research projects |
| CN115662554A (en) * | 2022-12-28 | 2023-01-31 | 北京求臻医疗器械有限公司 | Multi-group clinical trial subject matching method and device |
| CN116646041A (en) * | 2023-07-21 | 2023-08-25 | 北京惠每云科技有限公司 | Method and system for improving matching precision of clinical test subjects based on large model |
-
2021
- 2021-04-06 CN CN202110369172.6A patent/CN113139365A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114464327A (en) * | 2022-01-12 | 2022-05-10 | 上海柯林布瑞信息技术有限公司 | Method and device for screening subjects in clinical scientific research projects |
| CN115662554A (en) * | 2022-12-28 | 2023-01-31 | 北京求臻医疗器械有限公司 | Multi-group clinical trial subject matching method and device |
| CN116646041A (en) * | 2023-07-21 | 2023-08-25 | 北京惠每云科技有限公司 | Method and system for improving matching precision of clinical test subjects based on large model |
| CN116646041B (en) * | 2023-07-21 | 2023-11-21 | 北京惠每云科技有限公司 | Method and system for improving matching precision of clinical test subjects based on large model |
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